C-MAPSS Turbofan Engine Degradation Simulation Dataset 核心 · 已核验
atlas:c-mapss-dataset
NASA 用 C-MAPSS 仿真器生成的涡扇发动机退化仿真数据集(FD001-FD004 四个子集, 工况与故障模式组合不同),RUL 预测领域引用最多的基准数据集。
- 落地页
- https://ieee-dataport.org/documents/c-mapss-dataset
- 许可证
- CC-BY-4.0 (判读置信:inferred)
- 国内可访问性
-
国内直连:可达 (2026-07-11 检测)
代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。 - 发布年份
- 2025
- 发布方
- NASA Prognostics Center of Excellence
- 别名
- C-MAPSS / CMAPSS / NASA Turbofan
- 设备类型
aero_engine- PHM 任务
rul_predictiondegradation_trend_prediction- 跑至失效
- 是
分发点
| ieee_dataport | https://ieee-dataport.org/documents/c-mapss-dataset | |
| phm_society | https://data.phmsociety.org/nasa/ | NASA PCoE 镜像 |
数据概要
文本矩阵(单元号/循环数/3 工况设定/21 传感通道),train/test/RUL 标签三件套
故障工况
| description: 高压压气机(HPC)与风扇模块退化(仿真注入),FD003/004 含双故障模式fault_type: otherinduction: simulated_synthetic |
传感器
| sensor_type: otherobserved_property: temperaturemounting_note: 仿真传感通道(共 21 路:温度/压力/转速等,非物理传感器) |
| sensor_type: otherobserved_property: pressuremounting_note: 仿真传感通道 |
| sensor_type: otherobserved_property: rotating_speedmounting_note: 仿真传感通道(风扇/核心机转速) |
运行工况
| description: 海拔/马赫数/油门解析角组合,FD001/003 单工况,FD002/004 六工况condition_type: otheris_varying: True |
关联论文(663 篇,候选区未经人工核验;candidate_citation = 共引启发式候选关联,非使用断言)
- Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation 2008 · introduces
- Uncertainty aware predictive maintenance using a hybrid Transformer with Monte Carlo Dropout and conformal prediction 2026 · 候选关联(启发式)
- Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning 2026 · 候选关联(启发式)
- Physics-guided neural process with adaptive learning for uncertainty quantification of aero-engine remaining useful life 2026 · 候选关联(启发式)
- A novel interpretable deep autoencoder for health indicators construction and similarity-based RUL prediction 2026 · 候选关联(启发式)
- Remaining Useful Life Prediction in an Aerospace Engine: A Multivariable Fuzzy Time Series Classification Approach 2026 · 候选关联(启发式)
- Hybrid HHO–WHO Optimized Transformer-GRU Model for Advanced Failure Prediction in Industrial Machinery and Engines 2026 · 候选关联(启发式)
- Improving predictive maintenance: Evaluating the impact of preprocessing and model complexity on the effectiveness of eXplainable Artificial Intelligence methods 2025 · uses
- TTSNet: Transformer–Temporal Convolutional Network–Self-Attention with Feature Fusion for Prediction of Remaining Useful Life of Aircraft Engines 2025 · uses
- New Method for Remaining Useful Life Prediction Based on Recurrence Multi‐Information Time‐Frequency Transformer Networks 2025 · 候选关联(启发式)
- Predictive Maintenance Strategies for Aero‐Engines Considering Remaining Useful Life Prediction Interval 2025 · 候选关联(启发式)
- CONELPABO: composite networks learning via parallel Bayesian optimization to predict remaining useful life in predictive maintenance 2025 · 候选关联(启发式)
- Cluster edge-based active learning with batch queries for aircraft engine RUL prediction under label scarcity 2025 · 候选关联(启发式)
- A spatio-temporal hybrid method with multi-scale BiTCN and modified informer for remaining useful life prediction 2025 · 候选关联(启发式)
- A hybrid deep learning model for robust aeroengine remaining useful life prediction 2025 · 候选关联(启发式)
- Advanced Prognostic Health Management of Turbofan Engines: A Comprehensive Machine Learning Framework Using Real Flight Data 2025 · 候选关联(启发式)
- A comprehensive review on artificial intelligence driven predictive maintenance in vehicles: technologies, challenges and future research directions 2025 · 候选关联(启发式)
- Causal graph-based spatial–temporal attention network for RUL prediction of complex systems 2025 · 候选关联(启发式)
- Remaining useful life prediction considering correlated multi-parameter nonlinear degradation and small sample conditions 2025 · 候选关联(启发式)
- Predictive group maintenance using probabilistic prognostics and deep reinforcement learning 2025 · 候选关联(启发式)
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(PROV,6 条)
| source_url: https://ieee-dataport.org/documents/c-mapss-datasetretrieved_on: 2026-07-07asserted_by: human_curator |
| about_field: fault_conditionssource_citation: Saxena et al. (2008) PHM08, doi:10.1109/PHM.2008.4711414retrieved_on: 2026-07-07asserted_by: human_curator |
| about_field: publication_yearsource_url: https://api.datacite.org/dois/10.21227/q7dr-1b93retrieved_on: 2026-07-08asserted_by: automated_harvestnote: DataCite REST 元数据回填;仅填空字段,人工值不覆盖 |
| about_field: source_citation: 人工核验:zfbin(委托批准 2026-07-09)retrieved_on: 2026-07-09asserted_by: human_curatorconfidence_level: human_verifiednote: 晋升核心区。首晋升批次 01:KLS-005 手工填卡,实质核验于 KLS-005 完成(评审见 evidence/KLS-005/);委托代理执行 |
| about_field: china_accessibilitysource_citation: KLS-009 链接健康扫描(lychee 双通道)retrieved_on: 2026-07-11asserted_by: automated_harvestnote: 定期刷新标注,仅覆盖本字段;历史结果以最新扫描为准 |
| about_field: license_idsource_citation: 人工核验:zfbin(三问拍板 2026-07-11)retrieved_on: 2026-07-11asserted_by: human_curatorconfidence_level: human_verifiednote: 人工改写。license 记法归一 cc-by-4.0 → CC-BY-4.0(SPDX 规范 id,ADR-25 清账①,2026-07-11 用户拍板;许可语义不变) |